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Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop October 2005
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Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Dec 15, 2015

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Page 1: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Shimon Sarraf, Research Analyst

Center for Postsecondary Research, Indiana University

Bloomington

Session for NSSE “Veterans”

Regional NSSE User’s WorkshopOctober 2005

Page 2: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Overview

A Quick Survey

Reports Reviewed

Benchmarks Explained

2005 NSSE Dataset Details

Future NSSE Developments

Discussion & Questions

“NESSIE”

Page 3: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Goals

Two-way conversation

Share your experiences

Good preparation for Shimon’s second and third session

Page 4: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

A Quick Survey to Get Things Started….

Years of Participation in NSSE

Background of attendees

Page 5: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

The NSSE Reports:Respondent Characteristics

A quick snapshot of your institution

Data quality: Confirming if sample is representative

Response Rate and Sample Error

Page 6: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

2005 NSSE Response Rates by Carnegie Classification

33 32

3639

49

17 16

7

3

14

58

53

77

72

79

0

10

20

30

40

50

60

70

80

90

100

Doc-Extensive Doc-Intensive Masters Bac-Gen Bac-LA

Res

pon

se R

ate

(%)

Mean Min Max

Page 7: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

The NSSE Reports:Respondent Characteristics

What is Sampling Error?

Assumes random sampling

An estimate of the margin likely to contain your "true" score, for example:

If 60% of your students reply "very often" and the sampling error is ± 5%, it is likely that the true value is between 55% and 65%.

More respondents --> smaller sampling error

Page 8: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

2005 NSSE Sampling Error by Carnegie Classification

4 45

65

2 2 2 2 2

98

14

26

11

0

5

10

15

20

25

30

Doc-Extensive Doc-Intensive Masters Bac-Gen Bac-LA

Sam

pli

ng

Err

or (

%)

Mean Min Max

Page 9: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Variable

Bench-mark Class Mean Mean Sig a

Effect

Size b Mean Sig a

Effect

Size b Mean Sig a

Effect

Size b

1. Academic and Intellectual Experiences

FY 2.71 2.55 *** .19 2.84 *** -.15 2.86 *** -.18

SR 3.02 2.82 *** .22 3.15 *** -.16 3.16 *** -.18

FY 2.24 1.95 *** .40 2.30 ** -.07 2.28

SR 2.66 2.57 * .11 2.93 *** -.32 2.88 *** -.26

FY 2.45 2.48 2.69 *** -.25 2.65 *** -.21

SR 2.32 2.32 2.55 *** -.24 2.51 *** -.20

FY 2.98 2.86 *** .15 3.08 *** -.13 3.08 *** -.12

SR 3.31 3.19 *** .15 3.36 3.37 * -.08

FY 2.75 2.65 *** .11 2.76 2.77

SR 2.74 2.64 * .11 2.83 ** -.11 2.83 ** -.11

FY 2.14 2.19 2.01 *** .17 2.03 *** .15

SR 2.31 2.28 2.05 *** .35 2.08 *** .31

FY 2.36 2.30 * .07 2.42 ** -.08 2.40

SR 2.43 2.37 2.58 *** -.16 2.52 * -.09

FY 2.41 2.34 * .08 2.39 2.43

SR 2.82 2.80 2.76 2.77

FY 2.60 2.49 *** .14 2.54 * .08 2.57

SR 2.89 2.88 2.91 2.93

FY 1.72 1.72 1.68 1.72

SR 1.86 1.87 1.90 1.94 * -.08

FY 1.39 1.40 1.56 *** -.21 1.54 *** -.19

SR 1.63 1.54 * .11 1.80 *** -.19 1.77 *** -.15

Master's

ACL

ACL

ACL

ACL

ACL

ACLWorked with classmates outside of class to prepare class assignments

OCCGRP

Put together ideas or concepts from different courses when completing assignments or during class discussions

INTIDEAS

Come to class without completing readings or assignments

CLUNPREP

Worked with other students on projects during class

CLASSGRP

Worked on a paper or project that required integrating ideas or information from various sources

INTEGRAT

Included diverse perspectives (different races, religions, genders, political beliefs, etc.) in class discussions or writing assignments

DIVCLASS

Made a class presentation CLPRESEN

Prepared two or more drafts of a paper or assignment before turning it in

REWROPAP

NSSEville State UniversityNSSE 2005 Means Comparison Report

Asked questions in class or contributed to class discussions

CLQUEST

In your experience at your institution during the current school year, about how often have you done each of the following? 1=never, 2=sometimes, 3=often, 4=very often

NSSE 2005NSSEville

NSSEville compared with:

Selected Peers

a.

b.

c.

d.

e.

f.

g.

h.

Tutored or taught other students (paid or voluntary)

TUTOR

Participated in a community-based project (e.g. service learning) as part of a regular course

COMMPROJ

i.

j.

k.

The NSSE Reports:Means Comparison

Means, statistical significanc

e and effect sizes

Page 10: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

The NSSE Reports: Means Comparison

What is Statistical Significance?

Helps you answer the question, “How likely is it that the difference between my average student and the average student at [comparison group] is due to chance?

Significance determined by standard alpha values of p<.05, .01, or .001

Page 11: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Potential problem:As N becomes large, almost everything becomes statistically significant

How do we identify truly significant differences?

This is a question of …

practical significance

The NSSE Reports: Means Comparison

Page 12: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

The NSSE Reports: Means Comparison

What is Effect Size?

Practical significance of the mean difference

ES=mean difference/standard deviation

.2 is often considered small, .5 moderate, and .8 large (but rare!)

For example, while the difference in the means is statistically significant, the difference is so nominal that it doesn’t warrant further attention

Page 13: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Sele

cted

Pe

ers

Mas

ter's

NSS

E 20

05

Sele

cted

Pe

ers

Mas

ter's

NSS

E 20

05

2.71 2.55 2.84 2.86 .02 .02 .01 .00 .82 .82 .84 .84 1,329 3,004 19,955 47,746 .000 .000 .000 .19 -.15 -.18

2.24 1.95 2.30 2.28 .02 .01 .01 .00 .77 .74 .78 .78 1,327 3,001 19,949 47,729 .000 .009 .133 .40 -.07 -.04

2.45 2.48 2.69 2.65 .03 .02 .01 .00 .97 1.01 .97 .98 1,324 2,999 19,939 47,688 .371 .000 .000 -.03 -.25 -.21

2.98 2.86 3.08 3.08 .02 .02 .01 .00 .79 .84 .77 .78 1,324 2,999 19,933 47,691 .000 .000 .000 .15 -.13 -.12

2.75 2.65 2.76 2.77 .02 .02 .01 .00 .85 .89 .86 .87 1,326 2,998 19,932 47,678 .001 .841 .324 .11 -.01 -.03

2.14 2.19 2.01 2.03 .02 .01 .01 .00 .76 .79 .74 .74 1,324 2,994 19,911 47,644 .053 .000 .000 -.06 .17 .15

2.36 2.30 2.42 2.40 .02 .02 .01 .00 .81 .84 .81 .82 1,327 3,001 19,948 47,705 .031 .006 .071 .07 -.08 -.05

2.41 2.34 2.39 2.43 .02 .02 .01 .00 .81 .86 .85 .85 1,327 3,002 19,948 47,718 .012 .304 .428 .08 .03 -.02

2.60 2.49 2.54 2.57 .02 .01 .01 .00 .79 .79 .79 .80 1,232 2,835 19,150 45,820 .000 .010 .191 .14 .08 .04

1.72 1.72 1.68 1.72 .02 .02 .01 .00 .82 .84 .83 .84 1,232 2,835 19,160 45,844 .991 .067 .787 .00 .05 .01

1.39 1.40 1.56 1.54 .02 .01 .01 .00 .73 .72 .82 .81 1,232 2,832 19,156 45,828 .573 .000 .000 -.02 -.21 -.19

2.73 2.72 2.57 2.61 .03 .02 .01 .00 1.01 1.01 1.05 1.05 1,233 2,834 19,160 45,850 .846 .000 .000 .01 .15 .12

3.16 2.99 3.01 3.06 .02 .02 .01 .00 .80 .84 .85 .85 1,230 2,833 19,155 45,839 .000 .000 .000 .19 .17 .12

2.54 2.43 2.62 2.62 .02 .02 .01 .00 .88 .87 .86 .86 1,232 2,835 19,158 45,834 .000 .004 .001 .13 -.09 -.09

2.25 1.99 2.18 2.16 .02 .02 .01 .00 .85 .86 .89 .89 1,232 2,836 19,159 45,841 .000 .009 .001 .31 .08 .10

1.78 1.70 1.82 1.86 .02 .02 .01 .00 .82 .81 .85 .86 1,233 2,833 19,159 45,829 .004 .114 .003 .10 -.05 -.09

2.69 2.60 2.73 2.76 .02 .01 .01 .00 .75 .78 .80 .80 1,212 2,792 18,968 45,387 .000 .047 .001 .12 -.05 -.09

2.55 2.46 2.64 2.63 .02 .02 .01 .00 .83 .86 .83 .84 1,210 2,795 18,964 45,391 .005 .000 .000 .10 -.11 -.10

1.48 1.44 1.62 1.63 .02 .01 .01 .00 .76 .73 .84 .84 1,209 2,789 18,965 45,381 .150 .000 .000 .05 -.16 -.18

2.62 2.68 2.69 2.73 .02 .02 .01 .00 .85 .87 .86 .86 1,211 2,792 18,960 45,380 .034 .006 .000 -.07 -.08 -.13

2.52 2.71 2.52 2.60 .03 .02 .01 .00 1.03 1.00 1.01 1.02 1,212 2,790 18,956 45,366 .000 .969 .008 -.19 .00 -.08

2.76 2.85 2.70 2.77 .03 .02 .01 .00 .96 .94 .97 .97 1,212 2,790 18,959 45,375 .009 .035 .726 -.09 .06 -.01

2.96 2.88 2.90 2.85 .02 .02 .01 .00 .83 .86 .87 .88 1,207 2,781 18,859 45,096 .013 .016 .000 .08 .07 .12

3.12 3.05 3.04 3.09 .02 .01 .01 .00 .74 .76 .79 .78 1,207 2,781 18,849 45,068 .004 .000 .113 .10 .11 .05

2.82 2.81 2.81 2.87 .02 .02 .01 .00 .80 .84 .84 .84 1,206 2,779 18,835 45,049 .714 .628 .065 .01 .01 -.05

2.78 2.74 2.82 2.84 .02 .02 .01 .00 .83 .86 .85 .85 1,207 2,783 18,838 45,064 .136 .100 .020 .05 -.05 -.07

2.99 2.95 2.96 2.99 .02 .02 .01 .00 .81 .86 .85 .85 1,206 2,783 18,847 45,074 .198 .172 .873 .04 .04 .00

3.47 3.32 3.23 3.31 .03 .02 .01 .00 .97 .89 .96 .97 1,197 2,771 18,754 44,895 .000 .000 .000 .16 .25 .16

Sele

cted

Pee

rs

Sele

cted

Pee

rs

Sele

cted

Pee

rs

NSS

Evill

e

NSS

E 20

05

Mas

ter's

Mas

ter's

APPLYING

READASGN

MEMORIZE

ANALYZE

SYNTHESZ

EVALUATE

FACOTHER

DIVRSTUD

OOCIDEAS

DIFFSTU2

FACPLANS

FACIDEAS

FACFEED

WORKHARD

COMMPROJ

ITACADEM

EMAIL

FACGRADE

CLASSGRP

INTIDEAS

OCCGRP

TUTOR

REWROPAP

INTEGRAT

DIVCLASS

CLUNPREP

CLPRESEN

NSS

Evill

e

NSS

E 20

05

NSS

Evill

e

Mas

ter's

CLQUESTSe

lect

ed P

eers

Mas

ter's

NSSE 2005 Detailed Statistics

First-Year Students

Significance c Effect size d

NSS

Evill

e

NSS

E 20

05

NSSEvillecompared with:

NSSEvillecompared with:

NSS

E 20

05

NSSEville State University

Mean Standard Error of the Mean a Standard deviation b Number of respondents

The NSSE Reports:Detailed Statistics

Mean, N,

SEM, SD,

p-value, effect size

Page 14: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

The NSSE Reports:Detailed Statistics

What are Confidence Intervals?

CI = Mean +/- 2SEM

Multiplying the SEM by 2 creates a margin around the sample mean that is 95% likely to contain the true population mean.

More respondents smaller standard error of the mean (SEM), more precise estimate

Higher standard deviation greater SEM, less precise estimate

Page 15: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

The NSSE Reports:Frequency Distributions

Counts and

percentages for each response

option

Page 16: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Tip: Consider merging response options to create dichotomous variables (1/0)

Frequently = often + very often

Substantial = quite a bit + very much

The NSSE Reports:Frequency Distributions

Page 17: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

The NSSE Reports: New Features

Selected Peer Group

Including or Excluding your targeted oversample

Page 18: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

5 Benchmarks of Effective Educational Practice

Level of Academic Challenge

Active and Collaborative Learning

Student Faculty Interaction

Enriching Educational Experiences

Supportive Campus Environment

Page 19: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Level of Academic Challenge

Challenging intellectual and creative work is central to student learning and collegiate quality.

Institutions promote high levels of achievement by setting high expectations for student performance.

11 items include:

Preparing for class

Reading and writing

Using higher-order thinking skills

Institutional environment emphasizes academic work

Page 20: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Active and Collaborative Learning

Students learn more when they are more intensely involved in their education.

Collaborating with others prepares students to handle practical, real-world problems.

7 items include:

Asking questions in class

Making presentations

Working with other students on projects

Discussing ideas from readings or classes with others

Page 21: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Student Interactions with Faculty

Interacting with faculty show students first-hand how to think about and solve practical problems.

Teachers become role models and mentors for learning.

6 items include: Discussing assignments with a professor

Talking about career plans with faculty member or advisor

Getting prompt feedback on academic performance

Working with a faculty member on a research project

Page 22: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Enriching Educational Experiences

Students need learning opportunities that complement the goals of the academic program.

Provide opportunities to integrate and apply knowledge.

11 items include:

Experiencing diversity

Using technology

Participating in internships

Culminating senior experience

Page 23: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Supportive Campus Environment

Students perform better and are more satisfied at colleges that are committed to their success.

Does institution cultivate positive working and social relationships among different groups on campus?

6 items include:

Helping students achieve academically

Helping students cope with non-academic responsibilities

Quality of relationship between student and peers, faculty, and administrative personnel

Page 24: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

How are benchmark scores calculated?

1. Items are converted to a 100-point scale: [(response value – 1)/(total # of response values – 1)]*100

2. Part-time students' scores are adjusted on four Academic Challenge items.

3. Student-level scores are created for each group of items by taking the mean, as long as 3/5ths of the items were answered.

4. Institutional benchmarks are the weighted averages of the student-level scores.

Benchmarks of Effective Educational Practice

Page 25: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Benchmark Report

Level of Academic Challenge

Nesseville ConsortiumCarnegie National

First-Year 52.5 52.4 51.8 53.4

Senior 56.3 55.6 54.9 57.0

Challenging intellectual and creative work is central to student learning and collegiate quality. Colleges and universities promote high levels of student achievement by emphasizing the importance of academic effort and setting high expectations for student performance

25

35

45

55

65

75B

ench

mar

k S

core

s

Nesseville 52.5 56.3

Consortium 52.4 55.6

Carnegie 51.8 54.9

National 53.4 57.0

First-Year Senior

Level of Academic Challenge Items:

Preparing for class (studying, reading, writing, rehearsing, etc. related to academic program)

Number of assigned textbooks, books, or book-length packs of course readings

Number of written papers or reports of 20 pages or more; number of written papers or reports of between 5 and 19 pages; and number of written papers or reports of fewer than 5 pages

Coursework emphasizing analysis of the basic elements of an idea, experience or theory

Coursework emphasizing synthesis and organizing of ideas, information, or experiences into new, more complex interpretations and relationships

Coursework emphasizing the making of judgments about the value of information, arguments, or methods

Coursework emphasizing application of theories or concepts to practical problems or in new situations

Working harder than you thought you could to meet an instructor's standards or expectations

Campus environment emphasizing time studying and on academic work

Page 26: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Benchmark recalculation reports (04 &

05):

Driven by new calculation process that began for the 04 administration

Multi-year Comparisons EEE: not comparable because of response

set change

SFI: comparable by removing “research” item

Benchmarks of Effective Educational Practice

Page 27: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Institutions can use the student-level scores to:

Investigate what groups are more engaged than others on your campus.

Institutional subgroups (i.e., programs, departments)

Student sub-groups (i.e., gender, race)

Incorporate scale scores into predictive models of student outcomes (retention, g.p.a., satisfaction)

Benchmarks of Effective Educational Practice

Page 28: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

SatisfactionGeneral SatisfactionSatisfaction plus Quality of Campus Relationships

Campus EnvironmentEnvironmental EmphasesQuality of Campus Relationships

Gains FactorsPersonal/SocialGeneral EducationPractical Competence

Measurement Scales

Page 29: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Deep Learning Activities

Higher-Order Learning activities that require students to utilize higher levels of mental activity than those required for rote memorization (2b,c,d,e)

Integrative Learning activities that require integrating acquired knowledge, skills, and competencies into a meaningful whole (1d,e,i,p,t)

Reflective Learning activities that ask students to explore their experiences of learning to better understand how they learn

Page 30: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

NSSE Scalelets

Course Challenge

Writing

Active Learning

Collaborative Learning

Course Interaction

Out-of-Class Interaction

Gains (academic, personal, vocational)

Varied Experiences

Information Technology

Diversity

Support for Student Success

Interpersonal Environment

Page 31: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

2005 NSSE Dataset Details

What do you need to know to match your Institutional Report numbers?

“smpl05” (sampling type) use 1 (base sample), 2 (standard oversample),

3 (requested oversample) values… and 4 (targeted oversample) if “targetos” equals 1

“inelig” exclude all ineligible respondents

use those with values of 1 for “inelig”

Page 32: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Future Developments

Customized Report Engine

Archiving of reports and datasets online

Integrating new Carnegie classification

NSSE Knowledge Base

Page 33: Shimon Sarraf, Research Analyst Center for Postsecondary Research, Indiana University Bloomington Session for NSSE “Veterans” Regional NSSE User’s Workshop.

Discussion and Comments

Shimon SarrafResearch Analyst

Indiana University Center for Postsecondary Research1900 East 10th Street

Eigenmann Hall, Suite 419Bloomington, IN 47406

Ph: 812-856-2169

[email protected]

www.nsse.iub.edu